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The use of generative artificial intelligence (GenAI) in academia is a subjective and hotly debated topic. Currently, there are no agreed guidelines towards the usage of GenAI systems in higher education (HE) and, thus, it is still unclear how to make effective use of the technology for teaching and learning practice. This paper provides an overview of the current state of research on GenAI for teaching and learning in HE. To this end, this study conducted a systematic review of relevant studies indexed by Scopus, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The search criteria revealed a total of 625 research papers, of which 355 met the final inclusion criteria. The findings from the review showed the current state and the future trends in documents, citations, document sources/authors, keywords, and co-authorship. The research gaps identified suggest that while some authors have looked at understanding the detection of AI-generated text, it may be beneficial to understand how GenAI can be incorporated into supporting the educational curriculum for assessments, teaching, and learning delivery. Furthermore, there is a need for additional interdisciplinary, multidimensional studies in HE through collaboration. This will strengthen the awareness and understanding of students, tutors, and other stakeholders, which will be instrumental in formulating guidelines, frameworks, and policies for GenAI usage.

The genus of Clematis is a buttercup family (Ranunculaceae), recognized for its flowers and adaptable as an ornamental plant in colder climates. It contains around 300 species. Clematis has a variety of active phytoconstituents or secondary metabolites which are responsible for health benefits in human beings. Traditionally various species of the clematis genus used for centuries to treat various ailments including syphilis, bone disorders, gout, rheumatism, diuretics, wound healing, blood sugar control, blood diseases, spleen difficulties, leprosy, fevers, skin diseases. A systematic review of scientific electronic database and reference books were consulted to find all relevant literature for this work. This review discusses the ethno-medicinal usage, health advantages, phytoconstituents and bioactivities of clematis species.

Macroeconomic factors have a critical impact on banking credit risk, which cannot be directly controlled by banks, and therefore, there is a need for an early credit risk warning system based on the macroeconomy. By comparing different predictive models (traditional statistical and machine learning algorithms), this study aims to examine the macroeconomic determinants’ impact on the UK banking credit risk and assess the most accurate credit risk estimate using predictive analytics. This study found that the variance-based multi-split decision tree algorithm is the most precise predictive model with interpretable, reliable, and robust results. Our model performance achieved 95% accuracy and evidenced that unemployment and inflation rate are significant credit risk predictors in the UK banking context. Our findings provided valuable insights such as a positive association between credit risk and inflation, the unemployment rate, and national savings, as well as a negative relationship between credit risk and national debt, total trade deficit, and national income. In addition, we empirically showed the relationship between national savings and non-performing loans, thus proving the “paradox of thrift”. These findings benefit the credit risk management team in monitoring the macroeconomic factors’ thresholds and implementing critical reforms to mitigate credit risk.

The higher education (HE) sector benefits every nation’s economy and society at large. However, their contributions are challenged by advanced technologies like generative artificial intelligence (GenAI) tools. In this paper, we provide a comprehensive assessment of GenAI tools towards assessment and pedagogic practice and, subsequently, discuss the potential impacts. This study experimented using three assessment instruments from data science, data analytics, and construction management disciplines. Our findings are two-fold: first, the findings revealed that GenAI tools exhibit subject knowledge, problem-solving, analytical, critical thinking, and presentation skills and thus can limit learning when used unethically. Secondly, the design of the assessment of certain disciplines revealed the limitations of the GenAI tools. Based on our findings, we made recommendations on how AI tools can be utilised for teaching and learning in HE.

This research project delves into the critical issue of customer churn prediction within the context of a telecommunication company. Customer churn, the phenomenon where subscribers terminate their services, presents a significant challenge in an industry marked by intense competition and evolving customer preferences. The study employs advanced machine learning techniques to analyze vast volumes of historical customer data, comprising demographic information, service usage patterns, billing records, and past interactions with the company’s services. Through meticulous data preprocessing, feature engineering, and model selection, a predictive model is developed to forecast the likelihood of churn for individual customers. By accurately identifying customers at risk of churn, the telecommunication company can proactively implement targeted retention strategies. These strategies may include personalized incentives, tailored service offerings, proactive customer support, or loyalty programs. The effectiveness of the churn prediction model is evaluated using various metrics, ensuring its reliability and accuracy in real-world scenarios. Ultimately, the goal of this project is to equip the telecommunication company with actionable insights to reduce churn rates, optimize resource allocation, and foster long-term customer loyalty. This research contributes to enhancing the company’s competitive edge by enabling data-driven decision-making and proactive customer relationship management.

An accurate prediction of house prices is a fundamental requirement for various sectors including real estate and mortgage lending. It is widely recognized that a property value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighbourhood. Meeting the diverse housing needs of individuals while balancing budget constraints is a primary concern for real estate developers. To this end, we addressed the house price prediction problem as a regression task and thus employed various machine learning techniques capable of expressing the significance of independent variables. We made use of the housing dataset of Ames City in Iowa, USA to compare support vector regressor, random forest regressor, XGBoost, multilayer perceptron and multiple linear regression algorithms for house price prediction. Afterwards, we identified the key factors that influence housing costs. Our results show that XGBoost is the best performing model for house price prediction., Comment: 16 pages, Journal of Analytics

Background of the Study: The POCSO Act, 2012 is a comprehensive law to provide for the protection of children from the offences of sexual assault, sexual harassment, and pornography, while safeguarding the interest of the child at every stage of the judicial process by incorporating child-f A pre- experimental study to assess the effectiveness of structured teaching programme on knowledge regarding awareness of POCSO Act among adolescent girls 16-18 years in a selected school at Abhanpur, Chhattisgarh”. The aim of this study was to enhance the knowledge of adolescent girls regarding awareness of POCSO Act. In this study establish three objectives, first is to assess the pre-test and post test knowledge score regarding awareness of POCSO Act among adolescent girls16-18 years in a selected school at Abhanpur Chhattisgarh. Second objective is to evaluate the effectiveness of structured teaching programme on the knowledge regarding awareness of POCSO Act among adolescent girls (16-18 year) in a selected school at Abhanpur Chhattisgarh. Third objective is to find out the association between pre-test knowledge score regarding awareness of POCSO Act with their sociodemographic variables among adolescent girls 16-18years in a selected school at Abhanpur, Chhattisgarh. The research approach used for the study was quantitative research approach with pre-experimental research design and, research setting is Higher secondary school Abhanpur, target population is adolescent girls 16-18years using non-probability purposive sampling techniques, sample sizes 60 were selected, formulate the self-questionnaire nine socio-demographic, and fourteen content, and tool validated by seven experts, method of data collection by self-structured questionnaire, and data analysis by used descriptive and inferential statistics, in this study finding reliability of tool used karlpearson’s test and re -test method reliability is 0.84 which indicate prefect reliability, The findings of the study rev

Recent research has highlighted a key issue in speech deepfake detection: models trained on one set of deepfakes perform poorly on others. The question arises: is this due to the continuously improving quality of Text-to-Speech (TTS) models, i.e., are newer DeepFakes just 'harder' to detect? Or, is it because deepfakes generated with one model are fundamentally different to those generated using another model? We answer this question by decomposing the performance gap between in-domain and out-of-domain test data into 'hardness' and 'difference' components. Experiments performed using ASVspoof databases indicate that the hardness component is practically negligible, with the performance gap being attributed primarily to the difference component. This has direct implications for real-world deepfake detection, highlighting that merely increasing model capacity, the currently-dominant research trend, may not effectively address the generalization challenge.

Molecular electrocatalysts for CO2-to-CO conversion often operate at large overpotentials, due to the large barrier for C-O bond cleavage. Illustrated with ruthenium polypyridyl catalysts, we herein propose a mechanistic route that involves one metal center that acts as both Lewis base and Lewis acid at different stages of the catalytic cycle, by density functional theory in corroboration with experimental FTIR. The nucleophilic character of the Ru center manifests itself in the initial attack on CO2 to form [Ru-CO2](0), while its electrophilic character allows for the formation of a 5-membered metallacyclic intermediate, [Ru-CO2CO2](0,c), by addition of a second CO2 molecule and intramolecular cyclization. The calculated activation barrier for C-O bond cleavage via the metallacycle is decreased by 34.9 kcal mol(-1) as compared to the non-cyclic adduct in the two electron reduced state of complex 1. Such metallacyclic intermediates in electrocatalytic CO2 reduction offer a new design feature that can be implemented consciously in future catalyst designs.

Alcohols are one of the most common organic compound classes among natural and synthetic products. Thus, methods for direct removal of C−OH groups without the need for wasteful pre-functionalization are of great synthetic interest to unlock the full synthetic potential of the compound class. Herein, electroreductive C−OH bond activation and subsequent deoxygenative C−H and C−C bond formation of benzylic and propargylic alcohols are demonstrated along with mechanistic insights. Experimental and theoretical studies indicate that the reductive C−OH bond cleavage furnishes an open shell intermediate that undergoes a radical-polar crossover to the corresponding carbanion that subsequently undergoes protonation to furnish alkane products. Furthermore, we demonstrate that the carbanion can be trapped with CO2 to form arylacetic acids. The cathodic transformations are efficiently balanced by the anodic oxidation of sub-stoichiometric borohydride additives, a strategy that serves as a highly attractive alternative to the use of sacrificial metal anodes., QC 20240625

This study aims to develop a single integrated spoofing-aware speaker verification (SASV) embeddings that satisfy two aspects. First, rejecting non-target speakers' input as well as target speakers' spoofed inputs should be addressed. Second, competitive performance should be demonstrated compared to the fusion of automatic speaker verification (ASV) and countermeasure (CM) embeddings, which outperformed single embedding solutions by a large margin in the SASV2022 challenge. We analyze that the inferior performance of single SASV embeddings comes from insufficient amount of training data and distinct nature of ASV and CM tasks. To this end, we propose a novel framework that includes multi-stage training and a combination of loss functions. Copy synthesis, combined with several vocoders, is also exploited to address the lack of spoofed data. Experimental results show dramatic improvements, achieving a SASV-EER of 1.06% on the evaluation protocol of the SASV2022 challenge., Comment: Accepted by INTERSPEECH 2023. Code and models are available in https://github.com/sasv-challenge/ASVSpoof5-SASVBaseline

Presentation attack (spoofing) detection (PAD) typically operates alongside biometric verification to improve reliablity in the face of spoofing attacks. Even though the two sub-systems operate in tandem to solve the single task of reliable biometric verification, they address different detection tasks and are hence typically evaluated separately. Evidence shows that this approach is suboptimal. We introduce a new metric for the joint evaluation of PAD solutions operating in situ with biometric verification. In contrast to the tandem detection cost function proposed recently, the new tandem equal error rate (t-EER) is parameter free. The combination of two classifiers nonetheless leads to a \emph{set} of operating points at which false alarm and miss rates are equal and also dependent upon the prevalence of attacks. We therefore introduce the \emph{concurrent} t-EER, a unique operating point which is invariable to the prevalence of attacks. Using both modality (and even application) agnostic simulated scores, as well as real scores for a voice biometrics application, we demonstrate application of the t-EER to a wide range of biometric system evaluations under attack. The proposed approach is a strong candidate metric for the tandem evaluation of PAD systems and biometric comparators., Comment: To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence. For associated codes, see https://github.com/TakHemlata/T-EER (Github) and https://colab.research.google.com/drive/1ga7eiKFP11wOFMuZjThLJlkBcwEG6_4m?usp=sharing (Google Colab)

We present Malafide, a universal adversarial attack against automatic speaker verification (ASV) spoofing countermeasures (CMs). By introducing convolutional noise using an optimised linear time-invariant filter, Malafide attacks can be used to compromise CM reliability while preserving other speech attributes such as quality and the speaker's voice. In contrast to other adversarial attacks proposed recently, Malafide filters are optimised independently of the input utterance and duration, are tuned instead to the underlying spoofing attack, and require the optimisation of only a small number of filter coefficients. Even so, they degrade CM performance estimates by an order of magnitude, even in black-box settings, and can also be configured to overcome integrated CM and ASV subsystems. Integrated solutions that use self-supervised learning CMs, however, are more robust, under both black-box and white-box settings., Comment: Accepted at INTERSPEECH 2023

As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases1-3. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing4,5. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

A perforated liver abscess mimics hollow viscus perforations. It may be accompanied by pneumoperitoneum and peritonitis. A hollow viscus perforation appears to be the most common cause of gas under diaphragm. In about 10% of the cases, it can be due to rare abdominal and extra-abdominal causes. One of the causes could be intra-abdominal infection caused by gas-forming organisms. We are reporting a rare case of pneumoperitoneum resulting from an unruptured liver abscess in an old male with no comorbidity. An unruptured pyogenic right lobe liver abscess in a 70-year-old male was accompanied by X-ray flat plate abdomen features suggestive of free gas under the right hemidiaphragm. Culture of the pus drained from liver abscess grew Klebsiella sensitive to piperacillin and tazobactam, and antibiotic treatment was administered.

Deep learning has brought impressive progress in the study of both automatic speaker verification (ASV) and spoofing countermeasures (CM). Although solutions are mutually dependent, they have typically evolved as standalone sub-systems whereby CM solutions are usually designed for a fixed ASV system. The work reported in this paper aims to gauge the improvements in reliability that can be gained from their closer integration. Results derived using the popular ASVspoof2019 dataset indicate that the equal error rate (EER) of a state-of-the-art ASV system degrades from 1.63% to 23.83% when the evaluation protocol is extended with spoofed trials.%subjected to spoofing attacks. However, even the straightforward integration of ASV and CM systems in the form of score-sum and deep neural network-based fusion strategies reduce the EER to 1.71% and 6.37%, respectively. The new Spoofing-Aware Speaker Verification (SASV) challenge has been formed to encourage greater attention to the integration of ASV and CM systems as well as to provide a means to benchmark different solutions., Comment: 8 pages, accepted by Odyssey 2022

The performance of spoofing countermeasure systems depends fundamentally upon the use of sufficiently representative training data. With this usually being limited, current solutions typically lack generalisation to attacks encountered in the wild. Strategies to improve reliability in the face of uncontrolled, unpredictable attacks are hence needed. We report in this paper our efforts to use self-supervised learning in the form of a wav2vec 2.0 front-end with fine tuning. Despite initial base representations being learned using only bona fide data and no spoofed data, we obtain the lowest equal error rates reported in the literature for both the ASVspoof 2021 Logical Access and Deepfake databases. When combined with data augmentation,these results correspond to an improvement of almost 90% relative to our baseline system., Comment: Submitted to Speaker Odyssey Workshop 2022

ASV (automatic speaker verification) systems are intrinsically required to reject both non-target (e.g., voice uttered by different speaker) and spoofed (e.g., synthesised or converted) inputs. However, there is little consideration for how ASV systems themselves should be adapted when they are expected to encounter spoofing attacks, nor when they operate in tandem with CMs (spoofing countermeasures), much less how both systems should be jointly optimised. The goal of the first SASV (spoofing-aware speaker verification) challenge, a special sesscion in ISCA INTERSPEECH 2022, is to promote development of integrated systems that can perform ASV and CM simultaneously., Comment: Evaluation plan of the SASV Challenge 2022. See this webpage for more information: https://sasv-challenge.github.io

Multiple Choice Questions for any text image, either from long boring books or just some random hand written notes of yours. These questions are not from only that text image but also from internet, so that you can also encounter new questions i.e. extra facts, and who denies extra knowledge. Further it helps to revise and keep you up to date with your learning interests. In this paper we have aggregated different natural language processing technologies (NLP) and implemented them by their effective use for our project Automatic Quiz Generator in which text image is to be uploaded on API we provide so that the user gets the Multiple Choice Questions (MCQs) by searching online. We shall achieve that with the help of OCR, Keyword Extraction, Web Scraping, Django and different NLTK (Natural Language Tool Kit) tool.

This paper analyses trend and structure of workforce in Haryana from 1991 to 2011 by applying Crude Work Participation Rate (CWPR), Educational level of workers, Growth rate and share of workforce in non- agriculture sector. Our results show that there has been increase in the Crude work participation rate of both males and females in Haryana. The percentage of illiterate workers has declined and has increased for literate workers. Our results also show that there has been decline in growth of rural workforce and the share of workers in Non-Agricultural sector has increased.

Summary As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

This paper introduces RawBoost, a data boosting and augmentation method for the design of more reliable spoofing detection solutions which operate directly upon raw waveform inputs. While RawBoost requires no additional data sources, e.g. noise recordings or impulse responses and is data, application and model agnostic, it is designed for telephony scenarios. Based upon the combination of linear and non-linear convolutive noise, impulsive signal-dependent additive noise and stationary signal-independent additive noise, RawBoost models nuisance variability stemming from, e.g., encoding, transmission, microphones and amplifiers, and both linear and non-linear distortion. Experiments performed using the ASVspoof 2021 logical access database show that RawBoost improves the performance of a state-of-the-art raw end-to-end baseline system by 27% relative and is only outperformed by solutions that either depend on external data or that require additional intervention at the model level., Comment: Accepted to IEEE ICASSP 2022

Artefacts that differentiate spoofed from bona-fide utterances can reside in spectral or temporal domains. Their reliable detection usually depends upon computationally demanding ensemble systems where each subsystem is tuned to some specific artefacts. We seek to develop an efficient, single system that can detect a broad range of different spoofing attacks without score-level ensembles. We propose a novel heterogeneous stacking graph attention layer which models artefacts spanning heterogeneous temporal and spectral domains with a heterogeneous attention mechanism and a stack node. With a new max graph operation that involves a competitive mechanism and an extended readout scheme, our approach, named AASIST, outperforms the current state-of-the-art by 20% relative. Even a lightweight variant, AASIST-L, with only 85K parameters, outperforms all competing systems., Comment: 5 pages, 1 figure, 3 tables, submitted to ICASSP2022

Delhi, the national capital of India, experienced multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks in 2020 and reached population seropositivity of >50% by 2021. During April 2021, the city became overwhelmed by COVID-19 cases and fatalities, as a new variant, B.1.617.2 (Delta), replaced B.1.1.7 (Alpha). A Bayesian model explains the growth advantage of Delta through a combination of increased transmissibility and reduced sensitivity to immune responses generated against earlier variants (median estimates: 1.5-fold greater transmissibility and 20% reduction in sensitivity). Seropositivity of an employee and family cohort increased from 42% to 87.5% between March and July 2021, with 27% reinfections, as judged by increased antibody concentration after a previous decline. The likely high transmissibility and partial evasion of immunity by the Delta variant contributed to an overwhelming surge in Delhi.

Bryonia laciniosa is one such plant that is also known by the name of shivlingi as its seeds resemble the 'shivling' icon of Lord Shiva. This plant is mainly known for its seeds and is used in various ayurvedic drug formulations as an important ingredient. In Ayurveda and the folklore culture, shivlingi plant is used to treat various types of diseases such as cholera, bronchitis, constipation, diabetes, inflammation, paralysis, snake bites, abdominal diseases and mainly used as an aphrodisiac agent. In homeopathy, the B. laciniosa plant is used as an important ingredient in the formulation of anti-inflammatory drugs. Also, the plant is used to treat gastrointestinal, rheumatic, respiratory, liver, infectious diseases and metabolic disorders Besides this, shivlingi plant is associated with therapeutic potential which includes analgesic, antidiabetic, antioxidant, androgenic, antitumor, fertility and antipyretic properties. In this review article, the reported pharmacological properties of the plant have been described along with the plant utilization in folklore and ayurvedic medicinal system. Keywords: Ayurveda, Shivlingi, Folk view, Pharmacological properties

Artefacts that serve to distinguish bona fide speech from spoofed or deepfake speech are known to reside in specific subbands and temporal segments. Various approaches can be used to capture and model such artefacts, however, none works well across a spectrum of diverse spoofing attacks. Reliable detection then often depends upon the fusion of multiple detection systems, each tuned to detect different forms of attack. In this paper we show that better performance can be achieved when the fusion is performed within the model itself and when the representation is learned automatically from raw waveform inputs. The principal contribution is a spectro-temporal graph attention network (GAT) which learns the relationship between cues spanning different sub-bands and temporal intervals. Using a model-level graph fusion of spectral (S) and temporal (T) sub-graphs and a graph pooling strategy to improve discrimination, the proposed RawGAT-ST model achieves an equal error rate of 1.06 % for the ASVspoof 2019 logical access database. This is one of the best results reported to date and is reproducible using an open source implementation., Comment: Accepted in ASVspoof 2021 Workshop

The cues needed to detect spoofing attacks against automatic speaker verification are often located in specific spectral sub-bands or temporal segments. Previous works show the potential to learn these using either spectral or temporal self-attention mechanisms but not the relationships between neighbouring sub-bands or segments. This paper reports our use of graph attention networks (GATs) to model these relationships and to improve spoofing detection performance. GATs leverage a self-attention mechanism over graph structured data to model the data manifold and the relationships between nodes. Our graph is constructed from representations produced by a ResNet. Nodes in the graph represent information either in specific sub-bands or temporal segments. Experiments performed on the ASVspoof 2019 logical access database show that our GAT-based model with temporal attention outperforms all of our baseline single systems. Furthermore, GAT-based systems are complementary to a set of existing systems. The fusion of GAT-based models with more conventional countermeasures delivers a 47% relative improvement in performance compared to the best performing single GAT system., Comment: Submitted to INTERSPEECH 2021

Woodfordia fruticosa kurz is an herbal plant that belongs to the family Lythraceae. This plant is widely distributed throughout the tropical and subtropical regions of India, Sri Lanka, China, Malaysia, Indonesia, Japan and Pakistan. The plant possesses a long history of medicinal use. The flowers of Woodfordia fruticosa are recorded to possess potent therapeutic values. The various phytochemicals isolated from this plant are tannins, flavonoids, anthraquinone, glycosides and polyphenols. The extract of flowers and leaves are associated with useful therapeutic activities. These phytochemical compounds have many pharmacological properties such as antimicrobial, hepatoprotective, cardioprotective, antioxidant, antiulcer, immunomodulatory, antifertility and anti-tumor.  These pharmacological activities of Woodfordia fruticosa plant are also mentioned in the literature of ayurveda, yunani and other traditional systems of medicine. This review is intended to provide brief information on the plant Woodfordia fruticosa on the basis of chemical constituents, folk usage, ayurvedic usage, modern usage and its biological activities.  Keywords: Woodfordia fruticosa, antimicrobial, Ayurvedic, flavonoids, Octacosanol&nbsp

The custom of using plants for the therapeutic and dietary practices is as old as origin of humanity on the earth. One of the most ancient fruit plant is Punica granatum L., pomegranate belongs to Lythraceae family. The plant has a very rich ethnic history of its utilization around the world. The plant was used to symbolize prosperity, life, happiness, fertility etc. Apart from the ethnic beliefs associated with the plant, it is a well-considered plant based remedy used in treatment of many diseases in traditional system like Ayurveda and folk system of medicine. In Ayurveda it is esteemed as a Rasayana. It is used in many Ayurvedic polyherbal formulations which are used against many diseases. The plant consists of numerous phytochemical constituents in it such as polysaccharides, minerals, polyphenols, tannins, saponins, quinones, alkaloids, glycosides, coumarins, terpenoids, steroids etc. Each of the phytochemical constituent is associated with important therapeutic properties. This supper food is globally known for its high anti-oxidant potential. Other associated properties of this medicinal fruit plant are anti-microbial, hepatoprotective, cardioprotective, anti-diabetic, anti-cancer, immunomodulatory, anti-inflammatory, anti-hypertensive, anti-anemic etc. The aim of present review is to provide information related to phytochemistry, traditional uses in Ayurveda and folk medicinal system and therapeutic properties of Punica granatum L. Keywords: Dadim, Rasapanchak, Punicalagin, Punicic acid, Anti-oxidant.

When trained on the extensive collections of labeled data, Deep neural networks have demonstrated their potential for a good variety of supervised learning activities to deliver impressive performance. However, it takes a significant amount of money, time, and energy to construct such large data sets. In many practical situations, such tools might not be available, restricting the adoption and implementing many deep learning methods. Deep neural network-based semi-supervised learning implementations increasingly engaged in research to scale down the amount of labeled data needed, either by creating new methods or introducing current semi-supervised deep learning frameworks. To beat the need for giant annotated datasets, for more data-efficient deep learning methods. This article concentrated on a detailed analysis of semi-supervised learning focused on deep learning, accompanied by its description.

The thermodynamics of halogen bonding of a series of isostructural Group 10 metal pincer fluoride complexes of the type [(3,5-R-2-(POCOPtBu)-P-tBu)MF] (3,5-R-2-(POCOPtBu)-P-tBu=kappa(3)-C6HR2-2,6-(OPtBu2)(2) with R=H, tBu, COOMe; M=Ni, Pd, Pt) and iodopentafluorobenzene was investigated. Based on NMR experiments at different temperatures, all complexes 1-tBu (R=tBu, M=Ni), 2-H (R=H, M=Pd), 2-tBu (R=tBu, M=Pd), 2-COOMe (R=COOMe, M=Pd) and 3-tBu (R=tBu, M=Pt) form strong halogen bonds with Pd complexes showing significantly stronger binding to iodopentafluorobenzene. Structural and computational analysis of a model adduct of complex 2-tBu with 1,4-diiodotetrafluorobenzene as well as of structures of iodopentafluorobenzene in toluene solution shows that formation of a type I contact occurs.

Introduction: Carica papaya Linn. is a well-known medicinal plant that has been widely used for a long time to cure various types of infectious disease especially in South Asian countries. This study intended to identify the potential antibacterial properties in Carica papaya. Methods: The leaves of carica papaya was collected and thoroughly washed with tap water to remove dust, soil, birds dropping etc, within them. Then the leaves are dried under the shade for one week. The dried powder of papaya leaves was dissolved in 200ml methanol and it was thoroughly shaken to dissolve the powder into the solvent. Antibacterial activity was evaluated by using Agar Disk Diffusion Method. Results: The results of this study showed that both extracts showed moderate antibacterial activity against the test bacterial strains (E. colli). Conclusion: The crude methanolic extract of Carica papaya showed significant, antimicrobial activities, some of which support the traditional use of this plant in various diseases.

The threat of spoofing can pose a risk to the reliability of automatic speaker verification. Results from the bi-annual ASVspoof evaluations show that effective countermeasures demand front-ends designed specifically for the detection of spoofing artefacts. Given the diversity in spoofing attacks, ensemble methods are particularly effective. The work in this paper shows that a bank of very simple classifiers, each with a front-end tuned to the detection of different spoofing attacks and combined at the score level through non-linear fusion, can deliver superior performance than more sophisticated ensemble solutions that rely upon complex neural network architectures. Our comparatively simple approach outperforms all but 2 of the 48 systems submitted to the logical access condition of the most recent ASVspoof 2019 challenge., Comment: Submitted to Interspeech 2020 conference, 5 pages

Anti-spoofing for automatic speaker verification is now a well established area of research, with three competitive challenges having been held in the last 6 years. A great deal of research effort over this time has been invested into the development of front-end representations tailored to the spoofing detection task. One such approach known as constant Q cepstral coefficients (CQCCs) have been shown to be especially effective in detecting attacks implemented with a unit selection based speech synthesis algorithm. Despite their success, they largely fail in detecting other forms of spoofing attack where more traditional front-end representations give substantially better results. Similar differences were also observed in the most recent, 2019 edition of the ASVspoof challenge series. This paper reports our attempts to help explain these observations. The explanation is shown to lie in the level of attention paid by each front-end to different sub-band components of the spectrum. Thus far, surprisingly little has been learned about what artefacts are being detected by spoofing countermeasures. Our work hence aims to shed light upon signal or spectrum level artefacts that serve to distinguish different forms of spoofing attack from genuine, bone fide speech. With a better understanding of these artefacts we will be better positioned to design more reliable countermeasures., Comment: Accepted to Speaker Odyssey (The Speaker and Language Recognition Workshop), 2020, 8 pages

Heterobimetallic cofactors containing one manganese and one iron ion have recently been found within the di-metal carboxylate protein family. Herein we report the synthesis and characterization of three binuclear metal complexes with Fe-Fe, Mn-Mn, and Fe-Mn metal composition. All three complexes use the same ligand framework, the BPMP ligand (HBPMP=2,6-bis[(bis (-2-pyridylmethyl)amine) methyl]-4-methylphenol)) with two additional acetate ligands bridging the two metals. In terms of stability towards metal exchange, the Fe-Mn is more stable than the Mn-Mn complex but less stable than the Fe-Fe complex. Cyclic voltammetry shows that the Fe-Mn complex behaves markedly different than the homobimetallic complexes. The Fe-Mn complex also shows higher reactivity with O-2 than both the Fe-Fe and the Mn-Mn counterparts.

Objective I mmune-mediated inflammatory diseases (IMIDs) are heterogeneous and complex conditions with overlapping clinical symptoms and elevated familial aggregation, which suggests the existence of a shared genetic component. In order to identify this genetic background in a systematic fashion, we performed the first cross-disease genome-wide meta-analysis in systemic seropositive rheumatic diseases, namely, systemic sclerosis, systemic lupus erythematosus, rheumatoid arthritis and idiopathic inflammatory myopathies. Methods We meta-analysed similar to 6.5 million single nucleotide polymorphisms in 11 678 cases and 19 704 non-affected controls of European descent populations. The functional roles of the associated variants were interrogated using publicly available databases. Results Our analysis revealed five shared genome-wide significant independent loci that had not been previously associated with these diseases: NAB1, KPNA4-ARL14, DGQK, LIMK1 and PRR12. All of these loci are related with immune processes such as interferon and epidermal growth factor signalling, response to methotrexate, cytoskeleton dynamics and coagulation cascade. Remarkably, several of the associated loci are known key players in autoimmunity, which supports the validity of our results. All the associated variants showed significant functional enrichment in DNase hypersensitivity sites, chromatin states and histone marks in relevant immune cells, including shared expression quantitative trait loci. Additionally, our results were significantly enriched in drugs that are being tested for the treatment of the diseases under study. Conclusions We have identified shared new risk loci with functional value across diseases and pinpoint new potential candidate loci that could be further investigated. Our results highlight the potential of drug repositioning among related systemic seropositive rheumatic IMIDs.

Background: There has been an increased use of dressings containing silver in recent days. As many studies showings the impact of sustained-release silver foam dressings on microbial assay, there is a scarcity of studies on clinical and economical. Objectives: To investigate comparison between sustained-release silver dressings and 1% silver sulfadiazine (SSD) with respect to pain relief, its impact on wound healing, and hospital stay in patients with burns. Material and Methods: Hospital Based Prospective study was conducted in Burns Unit of Birla Hospital, Gwalior for a period of 1 year were 40 burns patient were evaluated. 40 cases were divided into 2 groups (Group A- patients who had dressing with 1% SSD and Group B- patients who had dressing with sustained-release silver dressing. Epi-info 7.0 software was used for analysis. Results: Days of stay in hospital are less in patients who were dressed with sustained release silver dressing (Group B) as compared to Group A(p<0.05). Percentage of wound healing increases from 5th day to 20th day in both the groups but significant increase was seen on 15th and 20th day in Group B. (P<0.05). Conclusion: Comparative study shows that sustained-release silver dressing has faster wound healing, lesser pain with earlier hospital discharge than SSD.

The substance on administration produces disturbance of the intellect faculty by virtue of its tamoguna, or substance which causes intoxication by its excessive consumption is called as Madya. Intake of Alcohol in improper manner for long time in excess quantity leads to the disease called Madatyaya or Panatyaya. In ayurvedic setting symptom Anidra (Insomnia) is difficult to treat, because the use of drugs containing opium or its derivative or cannabis, is like giving an addiction to treat addiction. So in present study “Clinical Efficacy Of Brahmi Vati On Insomnia W.S.R. To Madatyaya : A Randomized Control Trial” we try to conclude out the efficacy of drug not containing opium or cannabis on insomnia as a symptom of madatyaya. Group A contains Brahmi Vati, Jatamansi oil nashya and syp. M liv and Group B contains Syp. Shankhapushpi, , Jatamansi oil nashya and syp. M liv. The data collection, analysis and conclusion drawn from it is elaborated in this article.

Nasopharyngeal amyloidosis is an extremely rare benign tumour. It is divided into localized or systemic amyloidosis. It is more common in men. Clinical presentation includes nasal blockage, epistaxis and reduced hearing. Classical positive Congo red stain and appearance of apple green birefringence on polarized microscopy confirms the diagnosis of amyloidosis. We present a case of nasopharyngeal amyloidosis in a 44-year old lady who presented with acute hearing loss for 1 week with epistaxis mimicking nasopharyngeal carcinoma. Clinical examination showed a nasopharyngeal mass with biopsy proven AA amyloidosis. She is now cured of amyloidosis following endoscopic transnasal excision of tumour. We discuss on the similarity of presentation between nasopharyngeal carcinoma; the commonest malignant tumour in our region and the much rarer nasopharyngeal amyloidosis as well as highlighting the importance in early recognition of the latter in view of its known risk of systemic involvement.

In the current study a simple, precise, sensitive and accurate reversed phase liquid chromatography method was developed for the analysis and estimation of Lorcaserin HCL in bulk and tablet dosage form. The present study of Lorcaserin HCL was achieved by using Cosmosil C18 (250nm×4.6ID, Particle Size: 5 Micron) Column with mobile phase Methanol:10mM KH2PO4 Buffer (70:30) pH:3 at a flow rate 0.8ml/min with UV detection at 222nm. The retention time for Lorcaserin HCL was found to be 5.108 min. In Linearity the correlation coefficient (R2) for Lorcaserin HCL was found to be 0.9995, slope is 42071 and intercept was found to be 21966 which are well within the acceptance criteria. The mean percent recovery for Lorcaserin HCL at three different levels for 50%, 100%, and 150% was found to be 100.65%, 98.84% and 100.34%. The %RSD (NMT 2%). In precision study interday (RSD is 0.26%) and intraday (RSD is 0.29%) are found.  Forced degradation experiments was carried out by exposing standard form of Lorcaserin HCL for Acid-base hydrolytic, Oxidative, photolytic and thermal stress conditions. The method has been validated by System suitability parameters, Linearity, Accuracy and Percent recovery, Precision, Ruggedness, Robustness, LOD and LOQ. Keywords: Lorcaserin hydrochloride, RP-HPLC, Validation.

R. K. Narayan was leading writer of Indian literature in English. In his writing we saw various issues of Indian Society. He has touched some of the sensitive issues in his works like poverty, caste system, and lack of education. R.K. Narayan’s most famous novel entitled the Guide explores the aspirations Indian society and Indian youth. The movie Guide is an adaptation of the novel Guide presents aspiration of youth and Indian society. Literature always attracts cinema, various movies are based on the books. R. K. Narayan tried to show aspirations of Indian youth in the novel, this issue had attracted film maker. The paper presents the aspirations and beliefs of the main protagonist Raju. The main theme of the novel and movie is transformation of Raju from scoundrel to a sadhu. The paper concludes with a comparative study of The Guide and its cinematic adaptation.

Purpose We assessed the security and efficiency of intravenously injected agitated saline in conjunction with transthoracic echocardiography to identify recirculation in patients supported with a venovenous extracorporeal membrane oxygenation (VV ECMO) device. Materials and Methods We injected agitated saline 4 consecutive times separated by an interval of 5 minutes in 2 patients supported by VV ECMO. In both patients, the drainage cannula was placed in the left femoral vein, and the return cannula was placed in the right internal jugular vein. Echocardiography was performed during the injection and until the bubbles disappeared. The security of the method was assessed by evaluating the mechanical function of the ECMO and the efficiency of the oxygenator. The value of this method was assessed by visualizing the increase of inferior vena cava's echogenicity as well as by measuring the time required for this change to occur after the injection of agitated saline at different ECMO output levels. Results We did not observe any change in ECMO, oxygenation function, or the hemodynamic status of patients after the 4 injections of agitated saline. The echogenicity of the inferior vena cava increased more rapidly as the ECMO's output increased. The recirculation phenomenon was noted even with low levels of ECMO output (<2 L/min). Conclusions Transthoracic echocardiography in conjunction with agitated saline administration may be a safe and easily applicable method to evaluate a recirculation phenomenon in patients supported with VV ECMO., SCOPUS: ar.j, info:eu-repo/semantics/published

Commercially available domperidone orodispersible tablets (ODT) are intended for immediate release of the drug, but none of them have been formulated for sustained action. The aim of the present research work was to develop and evaluate orodispersible sustained release tablet (ODT-SR) of domperidone, which has the convenience of ODT and benefits of controlled release product combined in one. The technology comprised of developing sustained release microspheres (MS) of domperidone, followed by direct compression of MS along with suitable excipients to yield ODT-SR which rapidly disperses within 30 seconds and yet the dispersed MS maintain their integrity to have a sustained drug release. The particle size of the MS was optimized to be less than 200 μm to avoid the grittiness in the mouth. The DSC thermograms of MS showed the absence of drug-polymer interaction within the microparticles, while SEM confirmed their spherical shape and porous nature. Angle of repose, compressibility and Hausner's ratio of the blend for compression showed good flowability and high percent compressibility. The optimized ODT-SR showed disintegration time of 21 seconds and matrix controlled drug release for 9 h. In-vivo pharmacokinetic studies in Wistar rats showed that the ODT-SR had a prolonged MRT of 11.16 h as compared 3.86 h of conventional tablet. The developed technology is easily scalable and holds potential for commercial exploitation.

A series of Ru-II polypyridyl complexes of the structural design [Ru-II(R-tpy)(NN)(CH3CN)](2+) (R-tpy= 2,2': 6', 2''-terpyridine (R= H) or 4,4', 4''-tri-tert-butyl-2,2': 6', 2''-terpyridine (R= tBu); NN= 2,2'-bipyridine with methyl substituents in various positions) have been synthesized and analyzed for their ability to function as electrocatalysts for the reduction of CO2 to CO. Detailed electrochemical analyses establish how substitutions at different ring positions of the bipyridine and terpyridine ligands can have profound electronic and, even more importantly, steric effects that determine the complexes' reactivities. Whereas electron-donating groups para to the heteroatoms exhibit the expected electronic effect, with an increase in turnover frequencies at increased overpotential, the introduction of a methyl group at the ortho position of NN imposes drastic steric effects. Two complexes, [Ru-II(tpy)(6-mbpy)(CH3CN)](2+) (trans-[3](2+); 6-mbpy= 6-methyl- 2,2'-bipyridine) and [Ru-II(tBu-tpy)(6-mbpy)(CH3CN)](2+) (trans-[4](2+)), in which the methyl group of the 6-mbpy ligand is trans to the CH3CN ligand, show electrocatalytic CO2 reduction at a previously unreactive oxidation state of the complex. This low overpotential pathway follows an ECE mechanism (electron transfer-chemical reaction-electron transfer), and is a direct result of steric interactions that facilitate CH3CN ligand dissociation, CO2 coordination, and ultimately catalytic turnover at the first reduction potential of the complexes. All experimental observations are rigorously corroborated by DFT calculations.

The introduction of a simple methyl substituent on the bipyridine ligand of [Ru(tBu(3)tpy)(bpy)(NCCH3)](2+) (tBu(3)tpy = 4,4',4''-tri-tert-butyl-2,2':6',2''-terpyridine; bpy = 2,2'-bipyridine) gives rise to a highly active electrocatalyst for the reduction of CO2 to CO. The methyl group enables CO2 binding already at the one-electron reduced state of the complex to enter a previously not accessible catalytic cycle that operates at the potential of the first reduction. The complex turns over with a Faradaic efficiency close to unity and at an overpotential that is amongst the lowest ever reported for homogenous CO2 reduction catalysts.

Background of the study: Dexmedetomidine, a selective alpha-2 agonist which is being used widely in anesthesia and critical care practice. It has both sedative and analgesic property. The studies on intravenous usage of dexmedetomidine are numerous but not with intramuscular route. With this background we conducted a study on optimisation of intramuscular dosage of dexmedetomidine in patients undergoing functional endoscopic sinus surgery. Aim and objectives:To optimize the dosage of intramuscular dexmedetomidine in patients undergoing functional endoscopic sinus surgery for providing bloodless field for the surgeon. Materials and methods: We enrolled a total of 50 patients who have planned to undergo functional endoscopic sinus surgery under general anesthesia. After randomization, group D1 patients received intramuscular dexmedetomidine at the dosage of 0.5mcg/kg and group D2 1mcg/kg respectively. Hemodynamic parameters like blood pressure (MAP) and heart rate were recorded at regular intervals. The surgical field score was done by the operating ENT surgeon using Boerzaart scale. The targeted mean arterial blood pressure was kept between 55-65mmHg. The other parameters like additional requirement of opioid, usage of other vasodilators, time to emergence along with other side effects were noted peri-operatively. Results:After statistical analysis, the mean duration of surgery in both groups were 92±21.8 and 85±22.15 minutes respectively. The targeted mean arterial blood pressure was achieved in the patients who have received 1mcg/kg of dexmedetomidine (Group D2) intramuscularly. The surgeon felt satisfied with bloodless operating field in the patients who received 1mcg/kg of dexmedetomidine. Conclusion:From our study we concluded that 1mcg/kg of dexmedetomidine will be the adequate intramuscular dose for both premedication and hypotensive agent in functional endoscopic sinus surgery.